Authors' Reply: Long-Term Kidney Outcomes after Pediatric Acute Kidney Injury: Future Studies Need to Explore Further
Bibliographic record
Abstract
We thank Gao et al.1 for their interest in our recent JASN article “Long-Term Kidney Outcomes after Pediatric Acute Kidney Injury.”2 Their letter highlights limitations of population-based health administrative database research. However, we used robust statistical methods to mitigate observational research biases, including propensity score matching. Our study demonstrated a strong association between pediatric AKI (defined by diagnostic codes) and long-term CKD, KRT, and all-cause mortality. This persisted in sensitivity analyses defining AKI by standardized serum creatinine criteria among participants with available laboratory data. However, we could not evaluate the impact of AKI stage, duration, etiology, or extent of kidney function recovery (i.e., acute kidney disease) on long-term outcomes because of limited laboratory data. Previous studies report that AKI severity and duration are associated with higher risks of long-term CKD and hypertension.3,4 It remains unclear whether children with brief stage 1 AKI with complete functional recovery are at risk of kidney sequelae and require follow-up. Gao et al. also discuss the utility of cystatin C–based AKI definitions, particularly among neonates. Current AKI definitions are clearly limited by biologic and analytic variability of serum creatinine (e.g., age, sex, muscle mass, volume status, medications, and analytic method), which has led to substantial interest in novel AKI biomarkers. In 2023, urine neutrophil gelatinase–associated lipocalin became the first AKI biomarker approved in children by the US Food and Drug Administration. However, novel AKI biomarkers remain infrequently used in clinical practice and are not included in current AKI definitions. Cystatin C is a better marker of GFR than creatinine because it is less dependent on sex, race, muscle mass, and hydration status. However, cystatin C has not been shown to be better than serum creatinine for predicting pediatric AKI progression or long-term outcomes.5 Finally, we acknowledge potential residual confounding after propensity score matching because data on certain variables were missing, including nephrotoxic medications and iodinated radiocontrast exposures, illness severity measures (e.g., pediatric risk of mortality score), and AKI treatments. Future prospective observational studies are needed to evaluate risk factors of long-term adverse kidney outcomes after pediatric AKI. Up to 10% of hospitalized children develop AKI, and limited follow-up resources globally are a barrier to implementing post-AKI follow-up.6 Thus, clinical risk prediction tools should be developed to risk-stratify pediatric AKI survivors, identifying those most likely to develop CKD and hypertension and benefit from post-AKI kidney health surveillance.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.012 | 0.086 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.003 | 0.008 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.020 | 0.037 |
| Insufficient payload (model declined to judge) | 0.008 | 0.006 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".